Non‐alcoholic fatty liver disease in premenopausal women with polycystic ovary syndrome: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND AIM: Non-alcoholic fatty liver disease (NAFLD) and polycystic ovary syndrome (PCOS) are prevalent conditions sharing common pathogenic factors. We performed a systematic literature review and meta-analysis aiming to investigate the association between NAFLD and PCOS among premenopausal PCOS patients. METHODS: . Subgroup analyses and meta-regression for various covariates were performed. RESULTS: Of the 1833 studies retrieved, 23 studies with 7148 participants qualified for quantitative synthesis. The pooled result showed that women with PCOS had a 2.5-fold increase in the risk of NAFLD compared to controls (pooled OR 2.49, 95% confidence interval [CI] 2.20-2.82). In subgroup analyses comparing PCOS to controls, South American/Middle East PCOS patients had a greater risk of NAFLD (OR 3.55, 95% CI 2.27-5.55) compared to their counterpart from Europe (OR 2.22, 95% CI 1.85-2.67) and Asia (OR 2.63, 95% CI 2.20-3.15). Insulin resistance and metabolic syndrome were more frequent in the PCOS group (OR 1.97, 95% CI 1.44-2.71 and OR 3.39, 95% CI 2.42-4.76, respectively). Study quality and body mass index (BMI) were the only covariates that showed a relationship with the outcome in the meta-regression, with a regression coefficient of -2.219 (95% CI -3.927 to -0.511) and -1.929 (95% CI -3.776 to -0.0826), respectively. CONCLUSIONS: This meta-analysis indicates that premenopausal PCOS patients are associated with 2.5-fold increase in the risk of NAFLD, and BMI seems to be the main cofactor.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.026 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".